Expert Network in a Method and Apparatus for Keeping and Finding Information

ABSTRACT

An expert network in connection with keeping and finding information provides a textual search engine which, in response to a user query comprising a search term, uses a semantic vector to promote documents that contain other, closely related terms that strongly correlate with the search term. A social graph is generated for the user in which connections for the user comprise hyper-dimensional relationships based on semantic vectors that link the search term with a collection of the other, closely related terms. A personalized semantic vector for the user is applied to the social graph. The expert network applies the user&#39;s personalized semantic vector to locate experts in the user&#39;s social graph based on a user query topic. When a connection is considered an expert for the user, content that the connection kept is recommended to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional patent applicationSer. No. 61/784,860, filed Mar. 14, 2013, which application isincorporated herein in its entirety by this reference thereto.

BACKGROUND OF THE INVENTION

1. Technical Field

The invention relates to finding information that is contained in anenterprise or network. More particularly, the invention relates tokeeping and finding information.

2. Description of the Background Art

Finding information contained in an enterprise or network is frustratingand inefficient.

We bookmark a lot but we rarely re-use our bookmarks

People bookmark a lot but rarely visit their bookmark folder again, evenif it is exactly what they need. Instead, they spend time searching forwhat they have already saved.

Searching is not easy

Usually it takes a long time to actually find what we are looking for.

-   -   The Internet is packed with an enormous amount of information.        The more it grows, the harder it is to find what you really need        quickly.    -   Most people actually type only one or two words in the search        engine, which makes it extremely hard for the search engines to        come up with a good result.    -   Search engine optimization (SEO) is the process of affecting the        visibility of a website or a web page in a search engine's        “natural” or un-paid (“organic”) search results. In general, the        earlier (or higher ranked on the search results page), and more        frequently a site appears in the search results list, the more        visitors it will receive from the search engine's users. As an        Internet marketing strategy, SEO considers how search engines        work, what people search for, the actual search terms or        keywords typed into search engines and which search engines are        preferred by their targeted audience.        Human behavioral pattern: Amongst all the voices out there, we        trust the people we know most

These are our personal experts: the Tech God, the Foodie, the UrbanTraveller, the money guy, the designer, the best student in class, theparty dude, the athlete, the philosopher, the gamer, and the youngparent. Often, our close friends have already invested time researchingsomething that we are researching now. We rely on their expertise andfriendship for quick and trustworthy information.

In short: We spend a lot of time saving, or keeping, pages online tofind them easily later but, instead of using those bookmarks, we oftensearch for the same pages again and again. And if we can not find whatwe are looking for ourselves, we turn to our friends for help.

SUMMARY OF THE INVENTION

An embodiment of the invention provides an expert network in a methodand apparatus for keeping and finding information. A textual searchengine, in response to a user query comprising a search term, uses asemantic vector to promote documents that contain other, closely relatedterms that strongly correlate with the search term. For purposes of thediscussion herein, a semantic vector of a term comprises a globalfrequency of the other, closely related terms within a corpus that isused to compute the semantic vector relative to the search term. Asocial graph is generated for the user in which connections for the usercomprise hyper-dimensional relationships based on semantic vectors thatlink the search term with a collection of the other, closely relatedterms. A personalized semantic vector for the user is applied to saidsocial graph. The personalized semantic vector comprises a subset of thecorpus that is personal to the user. The personalized semantic vector isdetermined by taking into account only documents that the user hasstrong engagement with, and by which the user's personal semantics aredetermined when related to a specific term. An expert network appliesthe user's personalized semantic vector to locate experts in the user'ssocial graph based on a user query topic. The expert network identifiesthe user's intent and identifies experts for the term among the user'sconnections. When a connection is considered an expert for the user,content that the connection kept is recommended to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a screenshot showing an example of the results returned inresponse to a search query according to the invention;

FIG. 2 is a flow diagram showing a find engine according to theinvention;

FIG. 3 is a screenshot showing a user interface that includes a slideraccording to the invention;

FIG. 4 is a screen shot showing an overall view of a user interfaceaccording to the invention;

FIG. 5 is a screen shot showing a detailed view of a results and socialvalidation bar according to the invention;

FIG. 6 is a screen shot showing a chatter tooltip according to theinvention;

FIG. 7 is a screen shot showing a note tooltip according to theinvention;

FIG. 8 is a screen shot showing a top bar according to the invention;

FIG. 9 is a screen shot showing a top bar flow according to theinvention;

FIG. 10 is a screen shot showing a user tooltip according to theinvention;

FIG. 11 is a screen shot showing the slider concept and logic accordingto the invention;

FIG. 12 is a screen shot showing the basic anatomy of the slideraccording to the invention;

FIG. 13 is a screen shot showing comments flow according to theinvention;

FIG. 14 is a screen shot showing messages flow according to theinvention;

FIG. 15 is a screen shot showing a micro-find feature according to theinvention;

FIG. 16 is a block schematic diagram of a system architecture accordingto the invention;

FIG. 17 is a screen shot showing results returned in response to asearch query according to the invention;

FIG. 18 is a flow diagram showing a user search mechanism according tothe invention;

FIG. 19 is a flow diagram that shows user interaction with searchresults according to the invention;

FIG. 20 is a detailed flow diagram showing system processing of searchresults in connection with user behavior according to the invention;

FIG. 21 is a flow diagram showing the use of a semantic vector accordingto the invention;

FIG. 22 is a topological diagram according to the invention that showsan expert network according to the invention;

FIG. 23 is a topological representation of a document result setaccording to the invention; and

FIG. 24 is a block schematic diagram that depicts a machine in theexemplary form of a computer system within which a set of instructionsfor causing the machine to perform any of the herein disclosedmethodologies may be executed.

DETAILED DESCRIPTION OF THE INVENTION Overview

The invention is referred to herein as the KiFi method and apparatus forkeeping and finding information (“KiFi”), which is a shortened form ofthe tagline “Keep It, Find It.” The following discussion initiallydescribes KiFi at a high level and then proceeds to provide details ofpresently preferred embodiments of the invention. Applicantintentionally uses the second person in portions of the followingdiscussion for didactic purposes.

Keep it—No more bookmarks, just keeps

KiFi lets you and your friends easily Keep anything. With one click of abutton, the KiFi slider opens on your screen to let you keep that pagein your virtual, cloud-based brain. There is no need to tag the page,place it in a folder, rename it, etc. KiFi takes care of everythingautomatically. Because Kifi is cloud-based, anything digital can be keptonline: a Web page, a photo, an email, a Web doc.

Find It—Stop searching and start finding

When you search for anything online, your relevant Keeps appear asresults alongside the search engine's results. In addition, yourfriends' Keeps which are relevant to your search also appear on thepage.

For example: Let's say I want to buy a new a new SLR camera. I go toGoogle and type “buy a new digital slr camera,” and I get 43,500,000results, but I really hope the first page results helps me find myperfect camera. Instead I get results such as:

-   -   Don't buy DSLRs—3rd Gen Cameras are the Future    -   Digital SLR Buying Guide—CNET Reviews    -   Help! Buying new DSLR camera!        (and more mediocre results that I need to browse through).

And of course, thanks to SEO, the links I find take me to Amazon andGoogle Shopping, which means I need to keep on searching once I clickthrough. I'll probably spend a lot of time searching, researching, andvalidating many Websites or pages until I make my decision. I'llprobably also call a few of my photography-expert friends who I trustfor more information and validation about what they prefer before I makemy move.

With KiFi, I search Google and I see KiFi's trusted results 10immediately inside the Google results page (see FIG. 1). Quicklyscanning the results, I immediately see:

-   -   A few pages I kept using KiFi;    -   Older bookmarks on digital cameras I'd forgotten about, from the        last time I did research; and    -   A link kept by my friends Joe and Mike, who both know a lot more        than 1 do about SLR cameras, i.e. people I would've called to        get trusted information. I know that my friends spent the time        curating those links, slowly becoming experts I can rely on, and        more than that, they felt those links were important to keep and        share using KiFi.

In almost no time at all, I have trusted links that also leverage theresearch and expertise of my friends. With a quick search, I can Findthe right camera for me at the right price.

Conclusion: KiFi makes it easy to search and find what you're reallylooking for. It helps you Find the page that you want, the right productto buy, and provides the confidence needed to make the right decision,all socially validated by the people you trust.

How Kifi Works Socially Validated Search Results

Throughout our lives we seek validation from those we trust the most:the people we know. All of us have friends we call when we need help,information, and validation. We want to feel that we're doing the rightthing, and there is someone we trust who can validate our decisions,i.e. our friends, our personal experts.

After all, we're social animals. We each have an innate desire to belongto a social group. It is precisely because we value this sense ofbelonging so highly that the more other people find an idea, trend, orposition appealing or correct, the more correct that idea becomes in ourown minds.

How do we Use Social Validation in Our Daily Life?

For example: Let's say we plan a family vacation to Disneyland. We'llspend a lot of time researching before we hit the road. We'll go onlineand check for information, try to find reviews, look at photos andvideos hoping to find the best hotel, the best time to hit the road, theoptimal number of days before the kids just lose it, great restaurants,and maybe a babysitter so we can take the night off.

Picking our brain, we remember our good friends Gina and Andrew. Theyjust came back from a successful Disneyland vacation with their kids,and we know they spent a lot of time researching online and offlinetrying (like us) to find their dream vacation. Gina and Andrew are thetwo of the “Disneyland vacation experts” we need. We call them and aftera few minutes on the phone we get great information we can rely on, andeven some helpful tips.

In short: our friends spent the time researching; they just came backfrom their successful vacation; we know them personally; and we trustthem.

We are all experts in more than one thing, even if it's not part of ourLinkedIn page or Facebook profile. After all, an engineer can also be agreat cook, a mechanic, and even an expert parent. We curate knowledgeand experiences every day which are valuable for us, but wouldn't thisbe even more valuable for our friends and for the world?

The KiFi socially validated results do exactly this. KiFi displays asocial validation layer for every result:

-   -   Photo of yourself in case you kept that link;    -   Photos of your most relevant friends who kept the link;    -   Total number of friends who kept the link;    -   Total number of KiFi users who kept the link;    -   Globe icon, indicating that the page was kept by other KiFi        users who are not your friends;    -   KiFi Chatter icon, so you can quickly communicate with your        friends about their keeps; and    -   Note icon, indicating if you posted a note on the link.

Imagine a find engine built just for you, based on who you are, who yourfriends are, and constantly evolving with you. The most importantfeature of search engine results is how they are ranked. We all wishthat when we search, the first results would be the results we actuallywould like to find. This is an extremely complex problem to solvebecause there are so many Websites, pages, and pieces of informationthat are scattered everywhere. This results in millions of searchresults that most of the time are not relevant to what we are trying tofind. People rarely click on the second results page in Google, letalone the third or the fourth. And most of the time the results we seeare very similar.

For example: Let's say Mark and Zoe are searching for the word “Scala,”a new and exciting programming language, in Google. Most of the time theresults they get would be exactly the same. But Mark and Zoe areactually quite different, and they are looking for different things whenthey search for the same term. Mark is an 18 year old student who juststarted learning about Scala, while Zoe is a Scala veteran working for ayoung startup.

Because KiFi analyzes Mark's and Zoe's keeps, and many other parameters,the results they see from KiFi are completely different. Mark seesresults kept by himself; his classmate, who is the best student inclass; and other friends who are also novice Scala engineers. Zoe, onthe other hand, sees results kept by her co-workers, i.e. otherengineers, her friends who are Scala veterans like her, and maybe evenresults kept by Martin Odersky, who actually invented the Scalaprogramming language.

Conclusion

KiFi's trusted results are personalized for you, based on who you areand who your friends are. Because we are all so very different, theresults from KiFi are always very different. When searching for the samewords, we each most likely see different results. It's as though someonebuilt a search engine just for you, based on what's important for youand how you evolve in life, socially validated by people you trust.

Technology Find Engine

The KiFi Find Engine (see FIG. 2) is a live, online, InformationRetrieval, Machine Learning, and Artificial Intelligence system. TheFind Engine 20 feeds textual information 25 received via a textualsearch engine 21 and a clustering and classification engine 26, and userevents 29 and user profile information on KiFi and social networks 27received via a personal semantics module 22, to a curating andrecommendation engine 28, and produces the perfect search results thatmatch the use's needs 23, customized by the user's behavior and needs atthe time of search.

Textual Search and Semantic Vector

One of the key differentiators of the KiFi textual search engine is theuse of the Semantic Vector. The Semantic Vector (SV) refers to linking aterm, e.g. “Camping,” with a collection of closely related terms, e.g.“campground,” “RV,” “campfire,” “tent,” “woods,” “Yosemite,” “Fun,”“BBQ,” “Marshmallow,” and “Rock Climbing.” The textual search engine 21makes use of the semantic vector to promote documents that contain termsthat strongly correlate with the search term.

Recency is a consideration (see below). As I search, I see more and morethings. Some things are kept by people who are not in my immediatesocial circle, but they indicate somehow that the information isrelevant to me, for example based on my profile on KiFi and socialnetworks 27. Unlike the use of “like” in other systems, the Keep isembedded in the search results returned for a Web site. One of the keyvalues is the semantics. Thus, a Keep is unlike, for example, theFacebook “like” because the semantics of a Keep is for myself, not forothers, but others may benefit from the fact that I kept a particularpage. To indulge in a metaphor: a like is equivalent to one clappinghands, while a Keep is equivalent to: “I'm going to need it, so I'mgoing to keep it.” The later is of significance to others because of thesignificance it had to me, not because I broadcast my approval forothers to see.

Another thing to look at is the point of need, e.g. when you needsomething. If a friend choses to like a digital camera and I see it inFacebook, I am not at the point of need when I am going to Facebook. Iam just there to catch up with what my friends are up to. I may evenlook at the camera and go through the page. I probably will not buy it.However, when I am looking at Google and searching for a digital camera,I actually need it now. That is why I search. I actually actively searchfor this thing. An embodiment of the invention shows me right there andthen the thing that is most relevant to me. This is one benefit of usingthe semantics of a Keep versus a like. This aspect of the invention isreferred to herein as a Web gesture.

In this example, it is probably more likely that I would buy the camerawhen I am searching if I see a Keep of friend that I trust versus if Ijust see the camera on Facebook at a random page. This points out boththe a point of need issue and the trust that I give friends. Each ofwhich is a focus of the invention.

An embodiment of the invention commences with query processing, e.g. astandard search in documents 25. The searcher has a few terms, keywords,and the search engine tries to find them in documents using the searchengine 21, and then sorts the documents to attempt to determine whichdocuments are more relevant by clustering and classification 26. In anembodiment of the invention, if other people kept something, then it isrelevant; and if I am in the same geo-location as well, then it isprobably relevant to me. For example, consider paragliding. If there isa paragliding club next to my location, it may be more relevant to methan something else.

An embodiment of the invention also considers acceleration, i.e. ifthere is something trendy or recent, it is probably more relevant.

An embodiment of the invention also considers proximity in terms ofresults that are closer to each other, i.e. they probably relate better.If I'm looking for a dirt bike, the words “dirt” and “bike,” if they arein different parts of the document, probably do not mean the same thingas if they are in the same sentence.

An embodiment of the invention also considers socioeconomics. If aperson's friends are known to the system, for example via a socialnetwork, then knowing a person's friends tells us a lot about thatperson. Thus, an embodiment of the invention looks at the social networkaround a person and attempts to identify who they are and what they areprobably looking for, for example within the KiFi expert network 24,based upon the activity of the user's connections 30.

An embodiment of the invention also considers behavioral tracking, wherethe system tracks the behavior of people and tries to account for thebehavior with regard to the value a document may have for them. Forexample, how many times did people click on an article? Did people tointeract in their social network, or leave comments? Thus, actions suchas clicking, interacting, sharing, and engagement with the page, tellsus how the page is important.

An embodiment of the invention also provides comment systems, some ofwhich are private and some of which are public. The Keeps themselves maybe private or public. A person may not want to share a Keep, so it isprivate or anonymized so that other people do not see it.

Personal Semantic Engine

Once the user's information and behavior is studied by the Find Engine,it starts to personalize the search results. Web pages which are kept bythe individual or frequently interacted with are promoted.

KiFi personalized search customizes the Semantic Vector. The SemanticVector of a term may consist of global frequency of related terms. APersonalized Semantic Vector (PSV) uses only a subset of the corpus thatis personal to the user to compute a semantic vector. A PSV is computedby taking into account only documents that the user has strongengagement with, and by that we can understand the user's personalsemantics when related to a specific term.

For example: the term “Camping” discussed previously may be computedinto two distinct PSVs for different individuals:

-   -   “RV,” “Marshmallow,” “BBQ,” “Yosemite,” and “Fun” for the Family        Guy; and    -   “Campground,” “Campfire,” “Tent,” “Woods,” “Yosemite,” and “Rock        Climbing” for the Adventurous Hiker.

As we filter and boost search results using these PSV s, we discoverthat results for each individual are sorted differently according tothat person's personal semantics around the search term. For the FamilyGuy, Web pages that involve family camping activity are promoted, whilethe those pages are not prioritized for the Adventurous Hiker.

KiFi Expert Network

The KiFi Expert Network 24 is a layer on top of the KiFi Social Graph.In the KiFi Social Graph, basic connections are viewed ashyper-dimensional relationships based on semantic vectors. The ExpertNetwork locates experts in a user's social graph based on the topic theuser inquires about. Because we use the TV of the user, the topic ismore than a simple term, and the Expert Network understands thesemantics of the terms specifically for that user.

For example: when a user inquires about “Camping” we look at what theuser intends by “Camping” and we explore who would be the expert amongthat user's connections 30, in relation to what that user means by“Camping.”

Because “Camping” for that user may be related to “RV Style Camping,”then his connection that may be an expert about “Rock Climbing Camping”is not an expert for our user. On the other hand, a connection who is anexpert on “RV Style Camping” is considered an expert for our user andwhen he Keeps that content, the content that the connection kept wouldbe recommended to our user.

Because we examine multiple degrees of social connectedness, we canlearn about how a specific user is evaluated by his peers. Therefore,with a strong correlation of PSV for a few users, we can cross analyzetheir relationship and identify the expert among them.

For example: Consider a classroom that studies Napoleon Bonaparte.Assuming the students in the class are connected to each other overKiFi, their PSV in relation to Napoleon is similar. After all, they'reat the same level of knowledge as they learn together. Therefore, whenanalyzing the social graph of the users, we can identify the cluster ofstudents in the class with similar PSV as closely related. Going deeperinto that cluster of connections we can identify the users who areregarded by their peers as experts by following the networking signals.

In this example, a different person who studies Napoleon for many yearsand has written numerous academic articles about the topic would not bean expert on the Napoleon topic for this class because he is far tooadvanced, i.e. he has small relevancy to the high school level class.

As the Find Engine examines potential links to promote to users, itprefers to show the users links that were kept by his connections withstronger preference to the experts in that network.

In an embodiment of the invention, the social network is seen as anexpert system. There is the social network in general. Onto that, theinvention adds a layer of the strength of connections. Each connectionis given some strength. Maybe one person is seen as an expert inartificial intelligence, and another person is an expert in patent law,while still another is an expert in cooking. For example, when the topicbeing researched is cooking and the expert on artificial intelligenceadds Keeps about cooking, the system does not give him as much value asis given to the Keeps of the person who is an expert on cooking.

Thus, such social connections may have different values, depending onthe topic that is being accounting for. The system can take informationabout, for example, who you are and what value you bring by tracking thebehavior and tracking the profile of the people, who they are. Thus, theinvention develops a base of semantic knowledge. Embodiments connectthrough Facebook, LinkedIn, Twitter, etc.

An embodiment of the invention develops a vector, where wordssurrounding a term are considered. These words are looked at as thecontext for that term, and this occurrence is used to create a vector.Each term preferably has a vector. Thus, there is preferably a vectorfor every word in the document. The system also analyzes what a personkeeps. Accordingly, the term in one person's Keep has a different vectorthan that of another person's Keep. Thus, one can have a term with avector which is tuned for that person's Keeps. All of the documents theperson is interested in represent that person's interests, professionetc. Thus, the term “apple” for a computer scientist occurs more in acomputer science or computer industry context, but for a farmer it mayappear in an agricultural context. By comparing the vector of thedocument with the searcher's vector, the system can determine if theterm “apple” in a document is relevant or not. For purposes of thediscussion herein, this is referred to as a semantic vector, the use ofwhich allows the invention to personalize search results.

For further example, the term “banjo” may refer to performance for oneperson, but it points in a different direction with the search results,i.e. strings. In an embodiment of the invention, when we the search term“banjo” is entered into Google, a vector is applied to it, and thesearch results are better for what that person is looking because theinvention provides a mechanism, i.e. the vector, that recognizes thatthe term “banjo” has different meanings for different people.

The invention also can use the vector for social purposes because avector is given to each user. For example, because a vector for oneperson points toward photography, then that person may be better matchedsocially with another person having a similar vector, for example, foradvising each other, leaving comments, or keeping things and keeping oneach other's Keep lists.

For purposes of the discussion herein, it should be appreciated that theterm “social” refers to a productive and/or commercial social meaning,rather than a purely relationship-based meaning, where some individualsknow, for example, about plumbing, some individuals know about how toset up roofs, etc. Social herein is used in the sense of society.

Another embodiment of the invention concerns expert systems which createa social graph and links between people based on terms orclassifications. An embodiment of the invention takes classifications assemantic vectors, but it could be any classification, and applies valueson the classifications and links between people.

In this regard, it should be considered that, over time, a person'svector can shift. For example, the person can progress from an amateurbanjo player to an virtuoso. This concept also applies to the expertsystem. One may think that their friend here is an expert, e.g. inbanjos, but my friend has gotten better or worse. The relationshipbetween people changes over time and an embodiment of the inventiontracks and applies this change on the social graph in the expert system.This aspect of the invention is dynamic and adaptive. The system gathersthe activities from the social network that a person is part of, i.e.the behavior of the people in the social network, and applies theresulting information to the system.

Instead of a bookmark tool, which pulls down a bookmark, an embodimentof the invention provides a tool that comes down to each person's Keeps.One can enter comments with the tool for each Keep, such that the Keepis a carrier for additional information, e.g. meta-information, and thismeta-information is dynamic because people can add to it.

Additionally, the invention provides a self-organizing discussion groupbecause if someone is keeping something and there are other people whoare keeping the same thing, then a whole discussion goes on around thisone thing.

Furthermore, the invention provides a way to organize my bookmarksrelative to searches. People capture bookmarks and never use them. Withthe invention, if something is found to be interesting it can be savedas a Keep. If a new search is performed a few weeks later and the pagethat I kept comes up again it is promoted in the search results. Thus,the invention provides a way of bringing a page of interest back toone's attention. Accordingly, one aspect of a keep is to provide apersistent search object to which metadata may be attached.

There are two aspects to expert ratings. There is a global rating as anexpert, which is the average of all the different ratings given anindividual by others in all of the individual's capacities. For example,the individual in question may rate highly as a scientist and poorly asa cook. There is also a local rating. For example, if I am a scientistand my friend is a cook, then my rating as a scientist for my friend ishigh, but my rating for my friend as a cook is low. This rating can bederived from the overall rating given by others, but also by seeing whatthe individual keeps more of and less of. As a result, the rating of anindividual can start to drift. If the individual keeps everything, alower rating may be increased because that individual has manydemonstrated interests; if the individual never keeps anything, a lowerrating may be applied because the individual have not demonstrated anyinterests that would indicate any areas of expertise. In the invention,the learning mechanism is the use of Keeps.

Keeps are not bookmarks and can be derived from a person's profile onFacebook, LinkedIn, etc. Keeps can be what a person liked, the type ofinteractions a person has with a particular Web page, a page a personcomments on and/or spends a lot of time on, etc. Broadly, a Keepcomprises the behavior one has with the data in the system and/or whoone is personally.

The KiFi Slider is a Lot More than Just a Keep it Button

The KiFi Slider 31 (see FIG. 3, and discussed in greater detail below)can conveniently appear or be opened on every page while you browse theWeb. The slider is equipped with features designed to help easily keepand share documents, messages, ideas, notes with our friends and theworld.

The KiFi slider uses a smart algorithm called “The Slider Logic”(discussed below) that lets the slider open and suggest when to keep andeven what you should or shouldn't keep. Moreover, the Slider is equippedwith communication tools.

-   -   The messages feature allows us to easily send links and message        to a friend privately; and    -   The comments feature allows us to leave a review, comment or ask        a question publically, and also post them on our social        networks.

Everyone is a Contributor—the KiFi Social Networking Website

KiFi is a social networking Website intended to connect friends, family,and associates to easily find, discover, and manage Keeps.

To understand KiFi's value, let's use the analogy of Amishhome-building. Perhaps you've heard of how the Amish people cometogether as a community and use their combined individual levels ofexpertise to build a new house. They can do this in a matter of daysbecause everyone is a contributor. In fact, every individual in thecommunity adds value and expertise in several ways. The mailman might bethe one to install the plumbing and the windows, the school teachermight paint the walls and install the roof, while the carpenter alsomight do the landscaping and prepare the food.

Likewise, your social network is filled with experts in many things.KiFi facilitates your existing social networks, e.g. Facebook, Google+,etc., to help you discover what you're looking for, even if it'sdifficult to search for. On KiFi, users keep information that isimportant to them. This action is fundamentally different than likes,because your experts don't publish everything valuable to them onFacebook. What you keep on KiFi reveals so many dimensions of yourexpertise.

For example: Facebook only shows around 15-20% of the actual content allof your friends publish. Let's say that your friend Peter works at arestaurant, but he also knows a lot about hiking trails nearby.Unfortunately, because Peter doesn't publicly share all of his passionsonline, he can't easily help you. In fact, you might not even know allof what Peter is an expert in. Your Facebook friendship merelyrepresents what each of you publish, and that's if you even get to seeit. This ends up being very one-dimensional.

However, when your friends keep what is important to them, the KiFiengine connects you to their expertise. For your upcoming hiking trip,you see that Paul kept a local trail that you didn't know about. Now,you can find real value in your social network connections. In fact,KiFi doesn't limit who can help you to your most inner social network.Kifi lets you benefit from all the expertise of your friends. KiFi turnsyour social network (connections) into a helpful network (expertnetwork). Everyone adds value.

The 90/9/1 Rule

When thinking about how KiFi can transform a social network in an ExpertNetwork, consider the 90/9/1 Rule. Simply put, studies have shown thatin Internet communities 90% of users lurk, 9% of users contribute,through simple actions such as reply, like, and voting, and only 1% ofusers create. Yet when you flip that number, 90% of the content comesfrom 1% of the users, 10% comes from 9% of users, and the remaining 89%of users contribute nothing.

Kifi turns this dynamic on its head. Everyone is a contributor. All ofyour friends can help you, by simply using Kifi for themselves, becausethey want to be able to keep things and easily find them later.

-   -   Contributing is simple and easy. It keeps the same habit loops        that we already have when we bookmark.    -   Contributing is merely a side effect of keeping what is        important to you. Kifi serves you first, helping you easily find        what you found before.    -   KiFi rewards you. As you keep, you help your friends, which        brings you true social value. Imagine: What if you were able to        help your friends with things that you are passionate about,        without lifting a finger? How would you feel knowing you        constantly help your friends?    -   Quality>Quantity. KiFi doesn't flood you with millions of        results. We show you what's valuable to you now: your previous        valuable links and your expert friends' links.

Help Rank

Help Rank is an algorithm designed to rank the KiFi results based on howhelpful they are for each user.

The Help Rank algorithm is based on:

-   -   Your Keeps;    -   Rekeeps by friends/connections;    -   Social relationships:        -   How close you are with others based on your social graphs,        -   How much you have in common with others based on mutual            interest,        -   Recency, and        -   Online behavior: clicks, history, Keeps; and    -   Semantic vector.

Help Rank helps in new way. As opposed to Search Engine Optimization,which evaluates one universal set of what is the best information, i.e.Wikipedia as the default, and often unhelpful, first search result, HelpRank is unique to each KiFi user's values. The herein describedalgorithm gets you much closer to what you're actually trying to find.This is because Help Rank is dynamic: what's #1 in the search resultsfor me won't be #1 for you.

If the most important thing about a search engine is how things areranked, imagine the power of a search engine that ranks with your valuesin mind.

The invention can be thought of as a find engine that is intertwinedinto any search engine, such as Google, Bing, Ask, Pinterest, Amazon,etc. Finding is very important because it is not just finding the itemor the document. It is not just finding a link or finding somethingtechnical. Finding concerns the relationship of those who keepinformation and those who can then find the information.

Today, people are experts in many things throughout one day. This aspectof each individual, as tracked for example by their Keeps, all goes intothe find engine. However, finding a document is still not enough. Asearch in Google can find the right result because the result makessense for the query. As discussed above, the invention provides theindividual with the option to keep search results. A further aspect ofthe invention is referred to as help rank, where an individual keeps asite that was found through a friend because the friend had a Keep forthat information.

Keeps can include comments and messages. For example, an individualkeeps information about a camera on Amazon and comments as follows, “Thebest camera I ever bought.” Then, the comment is further validation ofthe relevance of the search result. Likewise, messages allow one towrite a message to a person who had a keep for, e.g. a camera, which hegets in his email, Facebook etc., which asks him, “Hey, did you buy thisthing that you kept?”

The invention applies a technique that recognizes that each person is anexpert in many, many things, and that each person is interested in many,many things. The use of Keeps informs both the individual who made aKeep when that individual performs a further search and informs acommunity of individuals by sharing that person's expertise and researchwith all other individuals who are looking to find similar informationand, at the same time builds an expert network or both knownindividuals, such as friends, and third parties, who are related by acommon interest. In such network, each person shares their expertise bywhat they keep.

Thus, you browse the Internet. You find the best information. You keepit. You comment on it. You micro-mark things in it. For me, in twoseconds I find all of the answers that I want just by looking for itbecause of your Keeps and those of others. This is not possible in atypical search engine.

In this example, some Keeps are from my friend. His results may be lowerin rank because of his level of expertise. Help rank further refines thesearch results by promoting results based upon the Keeps not only offriends, but also of others, some of whom are highly rated experts withregard to the topic in which I am interested. For example, consider anMIT professor who wrote a document about how to purify water using homeappliances. The professor posts the document on his blog. But who readshis blog? Not many people. If I search on Google, I probably cannoteasily find the professor's blog because of the way Google page rankworks, where one of the parameters that is used to rank results concernshow many Web sites point to the professor's blog. Page rank optimizesfor popularity, not necessarily relevance. Google also looks atinformation known about the searcher, for example from Google Plus, andmaybe searches that the searcher made, clicks in the search engine, etc.In this example, the MIT professor posted on his blog. In connectionwith the invention, a student of the professor looks at the professor'sblog. He is perhaps one of 20-100 people who look at this blog. And thestudent keeps it because it is important to him. And then, on thestudent's network, two other people found the professor's blog and theyalso kept it, and on and on. In connection with this example, furtherconsider a student in Zimbabwe. His water is horrible. Full of disease.But he has the Internet on his phone and he gets to the professor'sarticle because people kept it. And for him, his three best friends keptit. So, it was socially validated by them and then by all those peopleall the way back to the MIT professor. The student in Zimbabwe wouldnever find the professor's article on Google because it would have avery low ranking result number. Because the article was validated andpromoted by the Keeps of others it was lifted out of the noise andbrought into a deserved position of prominence in the search results.

Thus, the help rank feature of the invention takes advantage of peoplekeeping things by using the Keep feature of the invention. By keepinginformation, people help each other. In an embodiment of the invention,the system finds a common value, which can be a visualization of theexpert network. This is the social network and, on top of that, there isthe expert network, which is another form of social network. Thus, theexpert network is the network between people and how they are related toeach other in terms of expertise. It is a data-mined or filtered socialnetwork because the connections between people are based upon theexpertise that they share and the resonance of people regarding thatexpertise.

The finding aspect of the social network is dynamic to thatrelationship, where that relationship changes over time. For example, afriend may have expertise or interests in cooking, which may promotetheir keeps on this topic and which may rank them highly as an expertresource on this topic. Then my friend went to school to learn aboutpatents. Suddenly, the circles change and my friend's expertise inpatents grows because he went to school, and this is reflected in hiskeeps because he started keeping about patents. Thus, his relationshipwith me changes. Accordingly, there are circles of expertise within asocial network.

Distribution and Revenue Distribution Viral Channels:

KiFi is highly flexible in its ability to operate on any number of viralchannels:

-   -   Keeping—what you Keep can also be posted on any social network.    -   Commenting—when you Comment, this info can be posted on any        social network.    -   Messaging—not only can one Message within the KiFi Slider, but        the function can be used to send links to people who don't have        KiFi. When recipients would like to engage with the message,        they would have to register for KiFi.

Invite Friends Channels:

KiFi offers an array of simple, immediate channels to easily invitefriend networks:

-   -   Word of Mouth—KiFi results are better for you the more        friends/connection you add, and so word of mouth is accelerated        by the site's efficacy for individual users. Additionally, the        more friends you have, the more data you're exposed to, and thus        more social validation you have.    -   Connection to any Social Network and importing your friends.        KiFi makes it simple to import from Facebook, Google+, LinkedIn,        and more.    -   Connecting to your Contacts and importing your friends. It's        also easy to access the records we keep with email lists, phone        numbers, and digital address books.    -   Importing a contact pings a friend, thus prompting an opt in, to        invite him into the system.

Viral Channel for Partnerships:

Adding a Keep It button on a business homepage offers a significantviral channel for partners. Business partners are able to see quicklythe difference between how SEO ranks their Webpage versus KiFi. Whensites distribute KiFi to their users, they come up as the top result inany search asset when someone Keeps their page. In addition, all thefriends of that KiFi user see the site Kept and validated when theysearch for the partner site.

User Interface

FIGS. 4-15 are screen shots that depict various aspects of the KiFi userinterface.

FIG. 4 provides a general understanding of a KiFi search.

In FIG. 4, there is the KiFi Top Bar, which:

-   -   Indicates that the results below it are KiFi trusted results    -   On hover shows settings and filter options    -   On click minimizes or expands the KiFi results section;

The KiFi Results Types, where KiFi Results will be shown based on thefollowing sources:

-   -   Links you kept (public/private)    -   Links you and your friends kept (publicly)    -   Links ONLY your friends kept (publicly)    -   Links other users (which aren't your friends) kept (publicly)

KiFi Results types include:

-   -   Links to websites    -   Links to web apps locations, e.g. emails, Facebook messages,        Dropbox etc.    -   Links to online Documents, e.g. Google docs, PDFs    -   Links to Photos    -   Links to mobile apps; and

The Social Validation layer, where each link has a social validation barattached to it, and where the social validation bar includes:

-   -   Photo of yourself    -   Photos of your most relevant friends who kept the link    -   Total number of friends who kept the link    -   Total number of KiFi users who kept the link    -   Private indication in case the link was kept private    -   Globe icon, indicating this link was kept by other KiFi users        who are not you or your friend    -   KiFi Chatter icon, indicating if there are any comments or        messages from other KiFi users on that link    -   Note icon, indicating if you posted a note on the link

FIG. 5 provides a detailed view of the KiFi results and socialvalidation bar. FIG. 5 includes: Title of the result (taken from the URLtitle); Link URL; The Social validation bar; Chatter Indicator icon(more details below); Photo of myself (if I kept it); Photos of mostrelevant friends who kept the link; Private Keep indicator, whichreassures the user that he is the only person who see this Keep; Numberof friends who kept this link; and Number of people which are notfriends who kept this.

FIG. 6 shows the KiFi Chatter Tooltip, which appears on the socialvalidation bar only in case that any comments or messages were added onthat page:

-   -   KiFi Chatter is the communication component of KiFi. It allows        users to send and receive private messages to each other, and        post comments on pages for a public conversations;    -   Number of comments on this page, i.e. a click on this takes the        user to the page and opens the comments dialog on the slider;        and    -   Number of messages related to this page, i.e. a click on this        takes the user to the page and opens the messages dialog the        slider.

FIG. 7 shows a KiFi Note Tooltip, which appears only if the user left anote on this page:

-   -   The content of the note that the user left on this page, i.e. a        click on this takes the user to the page and opens the notes        dialog the slider.

FIG. 8 shows The KiFi Top Bar, which allows the user to enable filtersto refine and improve his search. The KiFi Top Bar:

Step 1: The top bar allows the user to enable filters to refine andimprove his search;

Step 2: Picking a filter type changes the results below (more details onthe next slide);

Step 3: Clicking on the “custom keeps” filter allows you to write nameof friends you whose keeps you want to search; and Adding a name of afriend immediately updates the search results for that person.

FIG. 9 shows The KiFi Top Bar flow, which allows the user to enablefilters to refine and improve his search. The Settings button takes theuser to the settings page related to KiFi search and preferences; MyKeeps filters by Keeps you made; Friends Keeps filters by Keeps thatyour friends kept; and Custom Keeps searches only within Keeps thatspecific friends kept.

FIG. 10 shows the KiFi User ToolTip, which appears when you hover overthe photo of a specific user. This includes: User name; User shortbio/title, e.g. pulled from social network and/or edited by him on hisprofile settings; User interests, e.g. interests determined from theuser Keeps but also can be added manually by the user; Social networksthat this user shares with me; and The total number of Keeps that theuser shares with me.

FIG. 11 shows the KiFi Slider concept and logic. The KiFi slider isKiFi's component that allows on-the-page information and interactionssuch as keeping, commenting, messaging, and taking notes. The slider'sLogic 110 is a set of smart rules based on the content of the page, theuser's behavior regarding this page, and his social network. These rulesassist us with deciding if and how the slider opens on the page.

For example, a page which a visited before by me, but I haven't kept it,does not pop the slider again, unless there's a new comment or messagewhen I get there again.

A sensitive site, such a site about medical issues, which was marked byus as sensitive does not cause the slider to pop even if it's the firsttime the user visited there. You may always force the slider to slide ina page by clicking the “K” icon on it.

FIG. 12 shows the “KiFi Slider” basic anatomy. This includes: User'sphoto (Avatar) showing the user who is connected to KiFi right now;User's full name; Slider closing button; Page Messages Indicator, whichshows how many message threads are there on this page (see messagesinteraction discussed elsewhere herein); Page Comments Indicator, whichshows how many comments are there on this page. (see commentsinteraction discussed elsewhere herein); Notes feature, which allows youto keep private comments, e.g. personal notes, about the page that onlyhe can see; Most relevant friends who kept this page photos, whichallows me to understand who are the friends who found this page valuableenough for them to keep it and promotes communication with them; Thekeep button, clicking on this adds the current page to your keeps, andif the page was already kept, you can unkeep it from the same button;and The keep button, clicking on this adds the current page to yourkeeps, and if the page was already kept, you can unkeep it from the samebutton.

FIG. 13 shows Comments flow, including: Comments number indicator, whereclicking on it takes the user to the comments screen; Comments view,which shows you all previous comments; Post a comment box, which allowsyou to post your own public comment and share it with you socialnetwork; The “Micro-find” (“look here”) feature (see more discussedelsewhere herein); Filter comments feature, with which you may filterall comments to see your only friends comments, yourself, or of aspecific friend; and “follow” (/unfollow) feature, where if a “followfeature” is turned on, you get a notification whenever a comment wasmade on this page.

FIG. 14 shows Messages flow, including: Compose a new message, whichallows you to start a new private discussion thread with your contacts;Recipients box, which pulls your KiFi Friends list, and supportsnon-KiFi recipients also, such as Facebook friends, email contacts, andother social network contacts; Message thread view, which shows you theentire conversation between you and the other participators; Info-bar,which gives more details about a specific message, such as when it wasposted, etc.; Reply box; Message number indicator, wherein clicking onit takes the user to the message center; Message thread, which shows youall message threads you are participating at on this page; Messagesfooter, which allows keeping/un-keeping and closing the messages screen;and The “Micro-find” (“look here”) feature (see more discussed elsewhereherein).

FIG. 15 shows The “Micro-find” (“look here”) feature (see more discussedelsewhere herein). The Micro find was created to help people point otherpeople to a specific area on that page. Assisting with the “find” value.

Step 1: Click on the micro find icon;

Step 2: Your page gets dim and a highlighting tool assists you withpicking the element you wish to highlight and point others to;

Step 3: Once picked, a “look here” link is created in your text box.

Results: If any other person clicks on this link, the page automaticallyscrolls and highlights the area you have selected.

Architecture

FIG. 16 is a block schematic diagram of a system architecture accordingto the invention. In FIG. 16, KiFi services, e.g. the Find Engine 20,provide a Find interface 166 and a Keep interface 168. In the presentlypreferred embodiment of the invention, system users 161 access the Findand Keep interfaces via the Internet 160, although the invention may beused in connection with other networks. The Find interface is used toaccess a content search index 162 and a personal search index 164, whilethe Keep interface accesses the personal search index. The contentsearch index lists Web pages document content 170, user generatedcontent 171, and semantic vectors 172 (described elsewhere); thepersonal search index lists user generated content 173, user explicitgestures 174, and user implicit gestures 175.

FIG. 17 is an example of the results returned in response to a searchquery according to the invention. In FIG. 17, the user has searched onthe query “apple” and the search has returned four results, three ofwhich are related to Apple Inc.

FIG. 18 is a flow diagram showing a user search mechanism according tothe invention. First, a user posits a query to the inventive system(180). The query is not incorporated with, for example, a Google search,but the system may include results that were previously obtained, forexample from a Google search. An embodiment of the invention providestwo indices, as shown on FIG. 16: an index for personal content and anindex for general content. General content refers to a document itself.Personal content refers to the user's notes, comments, and any othermeta-data that the user has personally related to the document.

The search is performed on these two indices (181; 182) and the resultsare merged (183). For example, there may be a document that does nothave the word “elephant” in it, but there is a comment that the userattached to the document that does contain the word “elephant.” In thiscase, the system retrieves that document as well. The user comment about“elephants” is in the personal index; the content index returnsdocuments with the word “elephant.” The indices themselves may be anyconvenient data structure, such as a table; further, embodiments of theinvention can use more than two indices.

As noted, the system merges the results and then categorizes (184) theresults into a plurality of categories which, in this embodimentcomprise three sets of results, i.e. what the user kept (185), what theuser's friends kept (186), and what others kept (187). For each of thesecategories the system processes the corresponding results in a series ofstages that begins with dumping (189, 190, 191) some of the results.Dumping can be thought of as demoting some of the scores. Next,tail-cutting (191, 192, 193) is used to decide upfront which of theresults are not relevant. Then some of the results are boosted (194,195, 196) based on other characteristics, for example number of Keepsthey had or some time recency, e.g. a recency boost. Those skilled inthe art will appreciate that each stage, for example, boosting can applyany of a number of parameters. For example, boosting can concern six,seven, or more different parameters.

At the end of this series of processing stages, the system merges theresults (197). The results are then augmented with personal informationabout the user (198), e.g. from a user profile, and the results are thenreturned to the client (199). In the example of FIG. 17, the resultsshown where produced according to the steps shown in FIG. 18.

FIG. 19 is a flow diagram that shows user interaction with searchresults according to the invention, and FIG. 20 is a detailed flowdiagram showing system processing of search results in connection withuser behavior according to the invention. For example, in an embodimentthe system boosts and dumps results on the basis of user interactionwith the results. It is preferred to amplify the results that have ahigh interaction and to dump the results that are consistently nothelpful. Even though the latter results may have high valuealgorithmically, the user insists on not using them, so they are dumped.This aspect of the invention goes to the idea that the same word couldmean different things to different people.

Thus, as shown in FIG. 17 for “apple” there is Apple Inc., pickingapples, the Apple ID account, and the Apple stock. The system collectsuser behavior when the user views the search results (200; FIG. 19),along with the encrypted query. For example, is the user engaged withthe page (202)? If not, the user signal is ignored (204); else, thesystem sends encrypted behavioral information to the system regardingthe user's personal temporal browsing history (206).

Thus, the system checks what the user does with the results (220; FIG.20). For example, the system identifies if and what among the resultsthe user clicks (222). Thus, the system collects information about whatthe user did and did not click on (224). There is also the notion of adead query, which means the user could have decided not to click onanything and moved on (226). The system collects this information aswell. As a result, the system either increases (228) or decreases (230)a docquery score for a document, i.e. the docquery score is increasedfor a selected document and decreased for a document that was notselected. In this way, the system scores specifically for therelationship between a particular document and a particular query. Thesystem then processes the score and produces a new docquery score thatis persisted for all real lists in the search index (232). The new scorefor each document is put in the personal search index (234) and thedocument is put into the document database (236). When the user performsa subsequent search, the most relevant results for that user arereturned, e.g. the user's Apple ID. Why? Because I have an account onApple.

FIG. 21 is a flow diagram showing the use of a semantic vector accordingto the invention. In FIG. 21, a query is issued by a user (240) andterms are extracted from the query (242). For each term (244, 245, 246),semantic vector is then constructed (250, 251, 252). The term is lookedup in an index of terms (247) and the term is filtered by both keeps(248) and history (249). Based on the Keeps and history, e.g. recency,of the term, the term is boosted (253, 255) and the resulting semanticvector scores are merged (259). The query is then run on the index(254). In this example, the query is run on the index for threedifferent terms, although any number of terms can be used in embodimentsof the invention. Each of the terms is then scored by matching theirsemantic vector with the term 1 results (256, 257, 258). Thereafter, theresults are merged per document (260) and used in the overall scoringsystem (262) (see FIG. 18).

FIG. 22 is a topological diagram according to the invention that showsan expert network for Dan. FIG. 22 is thus a personalized view of Dan'snetwork that shows the relationship between different people known toDan from Dan's perspective. Thus, Dan is connected to all of the fivepeople shown in FIG. 22 who are expert on the basis of a score whichindicates how Dan sees, for example, the strength of the relationshipbetween, Joe, Jill, Effi, Mery, and Sam.

In this example, Sam performs a search query, e.g. for banjos, across alarge document set and only receives six research results. Dan had keptfour of the results. The system helps Dan determine who is an expert onbanjos for purposes of Dan's query from Dan's point of view. The systemlooks at each document in the search results and checks how many clicks,e.g. how many times do Sam's friends visit each document. These resultsare multiplied by their rank. For example, Dan visited a page, kept it,and visited it ten times. In this example, the value of this documentmight result in it being ranked 10, the highest rank. This value isdetermined, for example, by counting the number of clicks or Keeps foreach document.

The next step is among Dan's experts. Who are the relevant experts forthis particular search? An embodiment of the invention aggregates thescores per person. If Sam has kept documents 1, 5, and 6, the number ofscores is aggregated, resulting in the maximum number in this embodimentof the invention, i.e. 10, because of the interaction score of thedocument times the relevancy of documents 1 plus 5 plus 6.

FIG. 23 is a topological representation of a document result setrelative to Dan and his friends. In this example, Mery kept one documentthat Dan kept, and the score is high, i.e. she gets 8. Effi also kept acouple of documents that Dan kept as well. So he gets a high score aswell. Jill did not have an intersection with Dan's results, she did notkeep anything that he kept, so she gets a zero. These values are basedupon each person's Keeps, but could also be based on other factors, suchas history, number of visits, a score based on interaction, combinationsof factors, etc.

Overall what is the interaction Dan has with the documents that Samkept? Is he an expert for banjos? This determination is made for each ofDan's friends. They have aggregated scores that indicate their personalexpertise, e.g. with regard to banjos. The system can take the topexperts, which are Sam and Effi, both of them have 10. The documentsthat they kept make them more valuable, so the system boosts theirranking. Because Sam is such an expert about Dan's ideas, then thedocuments he looked at should be boosted as well, perhaps even morebecause Dan missed them.

Effi could have landed on some of Dan's pages by accident, i.e. he keepstoo many things, which doesn't mean that he's an expert. He just keepsso much. So if he keeps so much, one way to deal with that is tonormalize Effi's keeps.

Thus, if Sam has only ten keeps and all of them were spot-on, then he'sprobably more valuable versus Effi who has 10,000 keeps and by accidenthe went to some of the same pages that Dan went to. Thus, the systemallocates scores to find the expert network. In an embodiment, a set ofrules is applied to determine who is the best expert and then to set upthe expert network.

Computer Implementation

FIG. 24 is a block schematic diagram that depicts a machine in theexemplary form of a computer system 1600 within which a set ofinstructions for causing the machine to perform any of the hereindisclosed methodologies may be executed. In alternative embodiments, themachine may comprise or include a network router, a network switch, anetwork bridge, personal digital assistant (PDA), a cellular telephone,a Web appliance or any machine capable of executing or transmitting asequence of instructions that specify actions to be taken.

The computer system 1600 includes a processor 1602, a main memory 1604and a static memory 1606, which communicate with each other via a bus1608. The computer system 1600 may further include a display unit 1610,for example, a liquid crystal display (LCD) or a cathode ray tube (CRT).The computer system 1600 also includes an alphanumeric input device1612, for example, a keyboard; a cursor control device 1614, forexample, a mouse; a disk drive unit 1616, a signal generation device1618, for example, a speaker, and a network interface device 1628.

The disk drive unit 1616 includes a machine-readable medium 1624 onwhich is stored a set of executable instructions, i.e., software, 1626embodying any one, or all, of the methodologies described herein below.The software 1626 is also shown to reside, completely or at leastpartially, within the main memory 1604 and/or within the processor 1602.The software 1626 may further be transmitted or received over a network1630 by means of a network interface device 1628.

In contrast to the system 1600 discussed above, a different embodimentuses logic circuitry instead of computer-executed instructions toimplement processing entities. Depending upon the particularrequirements of the application in the areas of speed, expense, toolingcosts, and the like, this logic may be implemented by constructing anapplication-specific integrated circuit (ASIC) having thousands of tinyintegrated transistors. Such an ASIC may be implemented with CMOS(complementary metal oxide semiconductor), TTL (transistor-transistorlogic), VLSI (very large systems integration), or another suitableconstruction. Other alternatives include a digital signal processingchip (DSP), discrete circuitry (such as resistors, capacitors, diodes,inductors, and transistors), field programmable gate array (FPGA),programmable logic array (PLA), programmable logic device (PLD), and thelike.

It is to be understood that embodiments may be used as or to supportsoftware programs or software modules executed upon some form ofprocessing core (such as the CPU of a computer) or otherwise implementedor realized upon or within a machine or computer readable medium. Amachine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine, e.g. acomputer. For example, a machine readable medium includes read-onlymemory (ROM); random access memory (RAM); magnetic disk storage media;optical storage media; flash memory devices; electrical, optical,acoustical or other form of propagated signals, for example, carrierwaves, infrared signals, digital signals, etc.; or any other type ofmedia suitable for storing or transmitting information.

Although the invention is described herein with reference to thepreferred embodiment, one skilled in the art will readily appreciatethat other applications may be substituted for those set forth hereinwithout departing from the spirit and scope of the present invention.Accordingly, the invention should only be limited by the Claims includedbelow.

1. A computer implemented method for keeping and finding information,comprising: a textual search engine, responsive to a user querycomprising a search term, using a semantic vector to promote any ofdocuments and sites that contain other, closely related terms thatstrongly correlate with said search term, wherein a semantic vector of aterm comprises a global frequency of said other, closely related termswithin a corpus that is used to compute said semantic vector relative tosaid search term; a processor generating a social graph for said user inwhich connections for said user comprise hyper-dimensional relationshipsbased on semantic vectors that link said search term with a collectionof said other, closely related terms; said processor applying apersonalized semantic vector for said user to said social graph, whereinsaid personalized semantic vector comprises a subset of said corpus thatis personal to said user, said processor determining said personalizedsemantic vector by taking into account only documents and sites thatsaid user has strong engagement with, and by which said user's personalsemantics are determined when related to a specific term; and an expertnetwork applying said user's personalized semantic vector to locateexperts in said user's social graph based on a user query topic: whereinsaid expert network comprises a network between individuals thatdescribes how they are related to each other in terms of expertise;wherein said expert network identifies said user's intent and identifiesexperts for said term among said user's connections; and wherein when aconnection is considered an expert for said user, documents and sitesthat said connection kept are recommended to said user.
 2. The method ofclaim 1, further comprising: said expert network examining multipledegrees of social connectedness to identify a peer evaluation for aspecific user.
 3. The method of claim 1, further comprising: said expertnetwork cross analyzing relationships between said user and said user'speers to identify an expert among said peers.
 4. The method of claim 1,further comprising: providing a find engine for examining links returnedin response to said query; and said find engine promoting links to saiduser when said links were kept by said user's connections; wherein astronger preference is shown for promoting links kept by experts saididentified by said expert network in user's social graph.
 5. The methodof claim 1, further comprising: said expert network developing a base ofsemantic knowledge in which each connection may have a different,topic-based value as an expert relative to said user.
 6. The method ofclaim 1, said semantic vector further comprising: a vector in whichwords surrounding a query term are used to identify a context for saidterm.
 7. The method of claim 1, further comprising: said processoranalyzing documents and sites among a user's query results, wherein saiduser ascribes significance to a document or site by keeping saiddocument or site; wherein a term in each document or site said user haskept has a different vector than that for the same term in the samedocument or site when said same document or site is kept by anotherindividual.
 8. The method of claim 1, further comprising: said processoranalyzing documents and sites among a user's query results, wherein saiduser ascribes significance to a document or site by keeping saiddocument or site; and said processor applying said personalized semanticvector for said user to said social graph in view of those documents andsites that said user kept.
 9. The method of claim 8, further comprising:said processor comparing the vector of a document or site resulting fromsaid query with said user's personalized semantic vector to determine ifa term in said document or site is relevant.
 10. The method of claim 1,further comprising: said processor applying said personalized semanticvector for said user to said social graph for social purposes.
 11. Themethod of claim 1, further comprising: said processor tracking shifts insaid user's personalized semantic vector over time; and in response tosaid shifts, said processor effecting corresponding changes amongconnections on said social graph in said expert network; whereinrelationships between individuals and their expertise relative to eachother within said expert network change over time.
 12. The method ofclaim 1, further comprising: said expert network providing expertratings; wherein said expert ratings comprise any of: a global rating asan expert, comprising an average of all different ratings given to auser by others in all of said user's capacities; and a local rating,which is derived from an overall rating given by others, and which isalso determined in connection with documents and sites to which saiduser ascribes significance by the act of keeping said documents orsites.
 13. The method of claim 12, further comprising: wherein saidexpert rating is a function of a user's demonstrated interests, asevidenced by said user's kept documents and sites.
 14. The method ofclaim 12, further comprising: said processor determining a help rank,where a user keeps a document or site that was found through anotherindividual who kept said document or site; wherein said help rankrefines said query results by promoting results based upon the keptdocuments and sites of both individuals known to said user and the keptdocuments and sites of others, at least some of whom are experts withregard to the user's topic.
 15. The method of claim 1, wherein eachconnection in said social graph shares expertise with in the expertnetwork by keeping documents and sites.
 16. The method of claim 1,wherein said expert network comprises circles of expertise within asocial network.
 17. The method of claim 14, further comprising: saidhelp rank aggregating each user's scores to a predetermined maximumbased on each user's interaction score with a document or sitemultiplied by relevancy of the document or site; wherein said values arebased at least in part upon documents and sites that each user kept; andwherein said aggregated score boosts each user's ranking as an expert.18. The method of claim 17, further comprising: allocating said scoresto find a best expert for the user's topic in the expert network.
 19. Anapparatus for keeping and finding information, comprising: a textualsearch engine, responsive to a user query comprising a search term,using a semantic vector to promote any of documents and sites thatcontain other, closely related terms that strongly correlate with saidsearch term, wherein a semantic vector of a term comprises a globalfrequency of said other, closely related terms within a corpus that isused to compute said semantic vector relative to said search term; aprocessor configured for generating a social graph for said user inwhich connections for said user comprise hyper-dimensional relationshipsbased on semantic vectors that link said search term with a collectionof said other, closely related terms; said processor configured forapplying a personalized semantic vector for said user to said socialgraph, wherein said personalized semantic vector comprises a subset ofsaid corpus that is personal to said user, said processor determiningsaid personalized semantic vector by taking into account only documentsand sites that said user has strong engagement with, and by which saiduser's personal semantics are determined when related to a specificterm; and an expert network applying said user's personalized semanticvector to locate experts in said user's social graph based on a userquery topic: wherein said expert network comprises a network betweenindividuals that describes how they are related to each other in termsof expertise; wherein said expert network identifies said user's intentand identifies experts for said term among said user's connections; andwherein when a connection is considered an expert for said user,documents and sites that said connection kept are recommended to saiduser.